Problem Statement

Research and Development wants help to determine new product ideas and pricing using existing product line as a benchmark.

Solution Summary

We’ve identified several product gaps in the existing product line including:

  1. Aluminum Over Mountain

  2. Aluminum Triathalon

The Data Science Team has developed a pricing model that uses predictive analytics to estimate the price of the new bicycle models based on the existing fleet. This ensures that new models are priced comparatively to other similar bicycles.

New product prediction for 2 new models:

  1. Trigger, Over Mountain with Aluminum Frame: $2,273

  2. Slice, Triathalon with Aluminum Frame: $1,902

Gap Analysis

Bike List

The current portfolio consits of 97 different bikes that were analyzed.

Gaps

The visualization segments the full bicycle product line by category and frame material. This exposes two product gaps:

  1. New Aluminum line of bikes in the Over Mountain Category

  2. New Aluminum line of bikes in the Triathalon

Price Prediction

New product prediction for 2 new models:

  1. Trigger, Over Mountain with Aluminum Frame: $2,273

  2. Slice, Triathalon with Aluminum Frame: $1,902

## [10:31:12] WARNING: amalgamation/../src/objective/regression_obj.cu:167: reg:linear is now deprecated in favor of reg:squarederror.
## [10:31:12] WARNING: amalgamation/../src/learner.cc:556: Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibility
New Model Attribute Slice Al 1 Trigger Al 1
.pred $1,902 $2,273
frame_material Aluminum Aluminum
category_2 Triathalon Over Mountain
model_base Slice Trigger
model_tier Ultegra Aluminum 1
black 0 0
hi_mod 0 0
team 0 0
red 0 0
ultegra 0 0
dura_ace 0 0
disc 0 0